Machine Learning Engineer

CareerUS Solutions

0 years

0 Lacs

India

Posted:1 day ago| Platform:

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Skills Required

learning design ml data regression clustering vision preprocess training mlflow airflow model monitoring testing ai research code engineering mathematics algorithms python tensorflow pytorch numpy visualization matplotlib aws gcp azure deployment git software processing spark hadoop containerization docker orchestration kubernetes

Work Mode

On-site

Job Type

Full Time

Job Description

Job Summary:

Machine Learning Engineer

Key Responsibilities:

Design, develop, and deploy machine learning models for classification, regression, clustering, recommendation, NLP, or computer vision tasks.
Collaborate with data scientists to prepare and preprocess large-scale datasets for training and evaluation.
Implement and optimize machine learning pipelines and workflows using tools like MLflow, Airflow, or Kubeflow.
Integrate models into production environments and ensure model performance, monitoring, and retraining.
Conduct A/B testing and performance evaluations to validate model accuracy and business impact.
Stay up-to-date with the latest advancements in ML/AI research and tools.
Write clean, efficient, and well-documented code for reproducibility and scalability.

Requirements:

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
Strong knowledge of machine learning algorithms, data structures, and statistical methods.
Proficient in Python and ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
Experience with data manipulation libraries (e.g., pandas, NumPy) and visualization tools (e.g., Matplotlib, Seaborn).
Familiarity with cloud platforms (AWS, GCP, or Azure) and model deployment tools.
Experience with version control systems (Git) and software engineering best practices.

Preferred Qualifications:

Experience in deep learning, natural language processing (NLP), or computer vision.
Knowledge of big data technologies like Spark, Hadoop, or Hive.
Exposure to containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
Familiarity with MLOps practices and tools.

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